We describe a methodology for making counterfactual predictions in settings where the information held by strategic agents and the distribution of payoff-relevant states of the world are unknown. The analyst observes behavior assumed to be rationalized by a Bayesian model, in which agents maximize expected utility, given partial and differential information about the state. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of the state and agents’ information about the state are held fixed. When the data and the desired counterfactual prediction pertain to environments with finitely many states, players, and actions, the counterfactual prediction is described by finitely many linear inequalities, even though the latent parameter, the information structure, is infinite dimensional. (JEL D44, D82, D83)

中文翻译：

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具有潜在信息的反事实

我们描述了一种在战略代理人持有的信息和世界上与收益相关的状态分布未知的情况下进行反事实预测的方法。分析人员观察到假设由贝叶斯模型合理化的行为，其中代理在给定有关状态的部分和差异信息的情况下最大化预期效用。在假设状态分布和代理关于状态的信息保持不变的假设下，需要对另一个战略环境中的行为进行反事实预测。当数据和期望的反事实预测与具有有限多个状态、参与者和动作的环境有关时，反事实预测由有限多个线性不等式描述，即使潜在参数、信息结构、是无限维的。(JEL D44, D82, D83)